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In arid and semi-arid environments, producing accurate maps of forest tree cover using optical remote-sensing data is essential to understand their spatial distributions and dynamics. In this respect, the current study aimed to explore the effectiveness of support vector machine (SVM), K nearest neighbors (KNN) and random forest (RF) machine learning (ML) algorithms to map the forest tree species of Ait Bouzid region (Central High Atlas, Morocco) based on Sentinel-2A data. The results from all models showed that about 19-28%, 21-27%, 16-24%, 15-18% and 0.3-0.32% of the area was covered by euphorbia, red juniper, cedar, holm oak, bare ground, and water body, respectively. According to the overall accuracy (OA) and kappa coefficient, the SVM classifier showed the highest OA (73%) and kappa (0.66) values, followed by KNN (OA=70%, kappa=0.62) and RF (OA=67%, kappa=0.59). Regarding LC classes, water, bare soil and holm oak could be identified with the producer's accuracy attaining 100%. At the same time, red juniper and cedar were the most challenging classes to determine for all ML classifiers, with the producer's accuracy of 40-50% and 40-67%. This study revealed the potential of ML approaches coupled with multispectral Sentinel-2A data for forest species cartography with high accuracy in arid areas. Furthermore, it provides crucial information about forest tree species distribution for developing forest management plans. [ABSTRACT FROM AUTHOR]